• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于嵌入式技术的高阶多智能体系统最优输出一致性。

Optimal Output Consensus of High-Order Multiagent Systems With Embedded Technique.

出版信息

IEEE Trans Cybern. 2019 May;49(5):1768-1779. doi: 10.1109/TCYB.2018.2813431. Epub 2018 Mar 20.

DOI:10.1109/TCYB.2018.2813431
PMID:29994166
Abstract

In this paper, we study an optimal output consensus problem for a multiagent network with agents in the form of multi-input multioutput minimum-phase dynamics. Optimal output consensus can be taken as an extended version of the existing output consensus problem for higher-order agents with an optimization requirement, where the output variables of agents are driven to achieve a consensus on the optimal solution of a global cost function. To solve this problem, we first construct an optimal signal generator, and then propose an embedded control scheme by embedding the generator in the feedback loop. We give two kinds of algorithms based on different available information along with both state feedback and output feedback, and prove that these algorithms with the embedded technique can guarantee the solvability of the problem for high-order multiagent systems under standard assumptions.

摘要

在本文中,我们研究了一种具有多输入多输出最小相位动力学形式的多智能体网络的最优输出共识问题。最优输出共识可以看作是具有优化要求的高阶智能体现有输出共识问题的扩展版本,其中智能体的输出变量被驱动以实现全局代价函数最优解的共识。为了解决这个问题,我们首先构建了一个最优信号发生器,然后通过将发生器嵌入反馈环中提出了一种嵌入式控制方案。我们给出了两种基于不同可用信息的算法,同时包括状态反馈和输出反馈,并证明了在标准假设下,这些带有嵌入式技术的算法可以保证高阶多智能体系统问题的可解性。

相似文献

1
Optimal Output Consensus of High-Order Multiagent Systems With Embedded Technique.基于嵌入式技术的高阶多智能体系统最优输出一致性。
IEEE Trans Cybern. 2019 May;49(5):1768-1779. doi: 10.1109/TCYB.2018.2813431. Epub 2018 Mar 20.
2
Optimal consensus seeking in a network of multiagent systems: an LMI approach.多智能体系统网络中的最优共识寻求:一种线性矩阵不等式方法。
IEEE Trans Syst Man Cybern B Cybern. 2010 Apr;40(2):540-7. doi: 10.1109/TSMCB.2009.2026730. Epub 2009 Oct 9.
3
Composite Backstepping Consensus Algorithms of Leader-Follower Higher-Order Nonlinear Multiagent Systems Subject to Mismatched Disturbances.领导者-跟随者高阶非线性多智能体系统在存在不匹配干扰下的复合反步一致性算法。
IEEE Trans Cybern. 2018 Jun;48(6):1935-1946. doi: 10.1109/TCYB.2017.2720680. Epub 2017 Jul 27.
4
Self-Triggered Leader-Following Consensus for High-Order Nonlinear Multiagent Systems via Dynamic Output Feedback Control.基于动态输出反馈控制的高阶非线性多智能体系统自触发领导-跟随一致性。
IEEE Trans Cybern. 2019 Jun;49(6):2002-2010. doi: 10.1109/TCYB.2018.2813423. Epub 2018 Mar 22.
5
Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection.一类具有抗扰的非线性多智能体系统的分布式优化。
IEEE Trans Cybern. 2016 Jul;46(7):1655-66. doi: 10.1109/TCYB.2015.2453167. Epub 2015 Aug 25.
6
Optimal Output Consensus of Heterogeneous Linear Multiagent Systems Over Weight-Unbalanced Directed Networks.加权不平衡有向网络上异构线性多智能体系统的最优输出一致性
IEEE Trans Cybern. 2024 Feb;54(2):1167-1177. doi: 10.1109/TCYB.2022.3191938. Epub 2024 Jan 17.
7
Consensus Control of a Class of Uncertain Nonlinear Multiagent Systems via Gradient-Based Algorithms.基于梯度算法的一类不确定非线性多智能体系统的共识控制。
IEEE Trans Cybern. 2019 Jun;49(6):2085-2094. doi: 10.1109/TCYB.2018.2819361. Epub 2018 Apr 9.
8
Necessary and Sufficient Conditions for Consensus of Second-Order Multiagent Systems Under Directed Topologies Without Global Gain Dependency.二阶多智能体系统在无全局增益依赖有向拓扑下一致性的充要条件。
IEEE Trans Cybern. 2017 Aug;47(8):2089-2098. doi: 10.1109/TCYB.2016.2616020. Epub 2016 Oct 21.
9
Data-Driven Terminal Iterative Learning Consensus for Nonlinear Multiagent Systems With Output Saturation.具有输出饱和的非线性多智能体系统的数据驱动终端迭代学习一致性
IEEE Trans Neural Netw Learn Syst. 2021 May;32(5):1963-1973. doi: 10.1109/TNNLS.2020.2995600. Epub 2021 May 3.
10
Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems.分布式神经网络控制用于不确定动态多智能体系统的自适应同步。
IEEE Trans Neural Netw Learn Syst. 2014 Aug;25(8):1508-19. doi: 10.1109/TNNLS.2013.2293499.

引用本文的文献

1
Distributed Adaptive Optimization Algorithm for High-Order Nonlinear Multi-Agent Stochastic Systems with Lévy Noise.具有列维噪声的高阶非线性多智能体随机系统的分布式自适应优化算法
Entropy (Basel). 2024 Sep 30;26(10):834. doi: 10.3390/e26100834.
2
Global Consensus of High-Order Discrete-Time Multi-Agent Systems with Communication Delay and Saturation Constraint.具有通信延迟和饱和约束的高阶离散时间多智能体系统的全球共识
Sensors (Basel). 2022 Jan 27;22(3):1007. doi: 10.3390/s22031007.